Image sensor systems often seek to compare image content between two or more data captures (frames) acquired by an image sensor. For example, such comparisons can be used to determine if any transient radiant sources are within the field of view (FOV) of the image sensor. Such transient radiant sources can, for example, include a temporary “turn on” signal such as a rocket launch, missile launch, anti-aircraft or artillery gun muzzle flash, explosions, etc. Transient radiant sources can also include rapidly moving sources such as in-flight missiles, rockets, or other ordinance. In such comparisons the image sensor system also often seeks to distinguish between a transient radiant source and a constant intensity or stationary source. In other applications, such comparisons can be used to track or detect movement of a person or other subject such as, for example, in video surveillance tracking, optical motion capture, or human computer interaction.
Conventionally, such comparisons are undertaken by image differencing. Image differencing simply subtracts the intensity data associated with a pixel in one frame from the intensity data associated with the corresponding pixel in another frame. However, while differencing is an important tool for comparing image content, it introduces a great deal of “noise” into the differenced image when used alone because it cannot account for motion, rotation, or vibration of the image sensor or host platform (e.g., an aircraft, vessel, vehicle, person, or other moving platform) over time. For example, if a constant radiation source moves through the FOV of the sensor due to motion of the sensor, the difference between pixel intensities from one frame and the next can be incorrectly or artificially high and the constant source may look like a moving object. Such incorrect differencing values can introduce unwanted errors into the frame comparison, corrupting the comparison data and any subsequent analysis.
Conventional frame registration has been used to eliminate some of the noise created by sensor motion. However, conventional frame registration techniques are highly problematic because they attempt to align frames based on readily identifiable parts of an image. This method is highly error prone because identifiable objects in an image can be difficult or impossible to locate depending on the contrast and consistency of the background image. Any failed alignment or misalignment then propagates through to subsequent frames and detections, exacerbating the problem. The difficulty in the application of this technique also increases in real time image processing systems where each frame of processing is required to take approximately the same amount of processing resources and time.
Another conventional frame registration technique applies a shifting scheme to iteratively move the frames relative to one another until a minimum difference is achieved for each pixel over a subset or the entirety of the focal plane. However, shifting methodologies are impracticable because performing the iterative shifting of each pixel requires a time-consuming draw on processing resources. Thus, this conventional technique does not deliver rapid results, especially for a size or weight limited host platform such as an aircraft or other vehicle. The difficulty in the application of this technique also increases in real time image processing systems where each frame of processing is required to take approximately the same amount of processing resources and time.